Invited Talks

About the speaker Björn Ommer

Björn Ommer is a full professor at University of Munich where he is heading the Computer Vision & Learning Group. Before he was a full professor in the department of mathematics and computer science at Heidelberg University and a co-director of its Interdisciplinary Center for Scientific Computing. He received his diploma in computer science from University of Bonn, his PhD from ETH Zurich, and he was a postdoc at UC Berkeley. Björn serves as an associate editor for IEEE T-PAMI. His research interests include semantic scene understanding and retrieval, generative AI and visual synthesis, self-supervised metric and representation learning, and explainable AI. Moreover, he is applying this basic research in interdisciplinary projects within neuroscience and the digital humanities. His group has published a series of generative approaches, including "VQGAN" and "Stable Diffusion", which are now democratizing the creation of visual content and have already opened up an abundance of new directions in research, industry, the media, and beyond.

About the speaker Lora Aroyo

I am a research scientist at Google DeepMind NYC where I work on Data Excellence for AI. My team DEER (Data Excellence for Evaluating Responsibly) is part of the Responsible AI (RAI) organization. Our work is focused on developing metrics and methodologies to measure the quality of human-labeled or machine-generated data. The specific scope of this work is for gathering and evaluation of adversarial data for Safety evaluation of Generative AI systems. I received MSc in Computer Science from Sofia University, Bulgaria, and PhD from Twente University, The Netherlands. I am currently serving as a co-chair of the steering committee for the AAAI HCOMP conference series and I am a member of the DataPerf working group at MLCommons for benchmarking data-centric AI. Check out our data-centric challenge Adversarial Nibbler supported by Kaggle, Hugging Face and MLCommons. Prior to joining Google, I was a computer science professor heading the User-Centric Data Science research group at the VU University Amsterdam. Our team invented the CrowdTruth crowdsourcing method jointly with the Watson team at IBM. This method has been applied in various domains such as digital humanities, medical and online multimedia. I also guided the human-in-the-loop strategies as a Chief Scientist at a NY-based startup Tagasauris. Some of my prior community contributions include president of the User Modeling Society, program co-chair of The Web Conference 2023, member of the ACM SIGCHI conferences board. For a list of my publications, please see my profile on Google Scholar.

About the speaker Linda Smith

Linda B. Smith, Distinguished Professor at Indiana University Bloomington, is an internationally recognized leader in cognitive science and cognitive development. Taking a complex systems perspective, she seeks to understand the interdependencies among perceptual, motor and cognitive developments during the first three years of post-natal life. Using wearable sensors, including head-mounted cameras, she studies how the young learner’s own behavior creates the statistical structure of the learning environments with a current focus on developmentally changing visual statistics at the scale of everyday life and their role in motor, perceptual, and language development. The work has led to novel insights about the statistics of self-generated experiences and their role in rapid learning and innovative generalization from sparse and limited experience and challenges current massive-data approaches in AI. The work also motivates her current efforts on defining and promoting a precision (or individualized) developmental science, one that determines the multiple causes and interacting factors that create children’s individual developmental pathways. Smith received her PhD from the University of Pennsylvania in 1977 and immediately joined the faculty at Indiana University. Her work has been continuously funded by the National Science Foundation and/or the National Institutes of Health since 1978. She won the David E. Rumelhart Prize for Theoretical Contributions to Cognitive Science, the American Psychological Association Award for Distinguished Scientific Contributions, the William James Fellow Award from the American Psychological Society, the Norman Anderson Lifetime Achievement Award, and the Koffka Medal. She is an elected member of both the National Academy of Sciences and the American Academy of Arts and Science.

About the speaker Jelani Nelson

Jelani Nelson is a Professor of Electrical Engineering and Computer Sciences at UC Berkeley, and also a Research Scientist at Google (part-time). He is interested in randomized algorithms, sketching and streaming algorithms, dimensionality reduction, and differential privacy. He is a recipient of the ACM Eugene L. Lawler Award for Humanitarian Contributions within Computer Science, a Presidential Early Career Award for Scientist and Engineers (PECASE), and a Sloan Research Fellowship. He is also Founder and President of AddisCoder, Inc., a nonprofit that provides algorithms training to high school students in Ethiopia and Jamaica.

About the speakers

Percy Liang is an Assistant Professor of Computer Science at Stanford University (B.S. from MIT, 2004; Ph.D. from UC Berkeley, 2011). His research spans machine learning and natural language processing, with the goal of developing trustworthy agents that can communicate effectively with people and improve over time through interaction. Specific topics include question answering, dialogue, program induction, interactive learning, and reliable machine learning. His awards include the IJCAI Computers and Thought Award (2016), an NSF CAREER Award (2016), a Sloan Research Fellowship (2015), and a Microsoft Research Faculty Fellowship (2014).

Jie Tang is a WeBank Chair Professor of Computer Science at Tsinghua University. He is a Fellow of the ACM, a Fellow of AAAI, and a Fellow of IEEE. His interest is artificial general intelligence (AGI). His research received the SIGKDD Test-of-Time Award (10-year Best Paper). He also received the SIGKDD Service Award. Recently, he puts all efforts into Large Language Models (LLMs): GLM, ChatGLM, etc.

Aakanksha led the effort on training large language models at Google Brain which led to the 540B PaLM model. Aakanksha has also been a core member of the Pathways project at Google. Prior to joining Google, Aakanksha led interdisciplinary teams at Microsoft Research and Princeton University across machine learning, distributed systems and networking. Aakanksha completed her PhD from Stanford University and was awarded the Paul Baran Marconi Young Scholar Award for outstanding scientific contributions in her doctoral thesis.

Angela Fan is currently a research scientist at Meta AI focusing on large language models. Previously, Angela worked on machine translation for text and speech, including projects such as No Language Left Behind and Beyond English-Centric Multilingual Translation. Before that, Angela was a research engineer and did her PhD at INRIA Nancy, where she focused on text generation.

Alexander "Sasha" Rush is an Associate Professor at Cornell Tech and a researcher at Hugging Face. His research interest is in the study of language models with applications in controllable text generation, efficient inference, and applications in summarization and information extraction. In addition to research, he has written several popular open-source software projects supporting NLP research, programming for deep learning, and virtual academic conferences. His projects have received paper and demo awards at major NLP, visualization, and hardware conferences, an NSF Career Award and Sloan Fellowship. He tweets at @srush_nlp.

About the speaker Christopher Ré

Christopher (Chris) Re is an associate professor in the Department of Computer Science at Stanford University. He is in the Stanford AI Lab and is affiliated with the Machine Learning Group and the Center for Research on Foundation Models. His recent work is to understand how software and hardware systems will change because of machine learning along with a continuing, petulant drive to work on math problems. Research from his group has been incorporated into scientific and humanitarian efforts, such as the fight against human trafficking, along with products from technology and companies including Apple, Google, YouTube, and more. He has also cofounded companies, including Snorkel, SambaNova, and Together, and a venture firm, called Factory. His family still brags that he received the MacArthur Foundation Fellowship, but his closest friends are confident that it was a mistake. His research contributions have spanned database theory, database systems, and machine learning, and his work has won best paper at a premier venue in each area, respectively, at PODS 2012, SIGMOD 2014, and ICML 2016. Due to great collaborators, he received the NeurIPS 2020 test-of-time award and the PODS 2022 test-of-time award. Due to great students, he received best paper at MIDL 2022, best paper runner up at ICLR22 and ICML22, and best student-paper runner up at UAI22.

About the speaker Susan Murphy

Susan A. Murphy is Professor of Statistics and Computer Science at Harvard University. Her research focuses on improving sequential decision making in health, in particular the development of online, real-time reinforcement learning algorithms for use in personalized digital health. She is a member of the US National Academy of Sciences and of the US National Academy of Medicine. In 2013 she was awarded a MacArthur Fellowship for her work on experimental designs to inform sequential decision making. She is a Fellow of the College on Problems in Drug Dependence, Past-President of Institute of Mathematical Statistics, and a former editor of the Annals of Statistics.